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Updated quick starts #1450

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Updated quick starts
SherinDaher-Runai Mar 15, 2025
a93001a
Updated quick start
SherinDaher-Runai Mar 18, 2025
1d09360
Update launching-workloads-with-gpu-memory-swap.md
SherinDaher-Runai Mar 18, 2025
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108 changes: 62 additions & 46 deletions docs/Researcher/scheduling/launching-workloads-with-dynamic-gpu-fractions.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -93,44 +93,52 @@ Before you start, make sure:

```sh
runai project set "project-name"
runai workspace submit "workload-name" --image gcr.io/run-ai-lab/pytorch-example-jupyter --gpu-memory-request 4G
--gpu-memory-limit 12G --large-shm --external-url container=8888
--name-prefix jupyter --command -- start-notebook.sh
--NotebookApp.base_url=/${RUNAI_PROJECT}/${RUNAI_JOB_NAME}
--NotebookApp.token=
runai workspace submit "workload-name" --image gcr.io/run-ai-lab/pytorch-example-jupyter --gpu-memory-request 4G \
--gpu-memory-limit 12G --large-shm --external-url container=8888 \
--name-prefix jupyter --command -- start-notebook.sh \
--NotebookApp.base_url=/${RUNAI_PROJECT}/${RUNAI_JOB_NAME} --NotebookApp.token=
```

=== "API"
Copy the following command to your terminal. Make sure to update the below parameters according to the comments. For more details, see [Workspaces API:](https://api-docs.run.ai/latest/tag/Workspaces)

```bash
curl -L 'https://<COMPANY-URL>/api/v1/workloads/workspaces' \ #<COMPANY-URL> is the link to the Run:ai user interface.
curl -L 'https://<COMPANY-URL>/api/v1/workloads/workspaces' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer <TOKEN>' \ #<TOKEN> is the API access token obtained in Step 1.
-H 'Authorization: Bearer <TOKEN>' \
-d '{
"name": "workload-name",
"projectId": "<PROJECT-ID>", '\ #The ID of the Project the workspace is running on. You can get the Project ID via the Get Projects API.
"clusterId": "<CLUSTER-UUID>", \ #<CLUSTER-UUID> is the unique identifier of the Cluster. You can get the Cluster UUID by adding the "Cluster ID" column to the Clusters view.
"projectId": "<PROJECT-ID>",
"clusterId": "<CLUSTER-UUID>",
"spec": {
"command" : "start-notebook.sh",
"args" : "--NotebookApp.base_url=/${RUNAI_PROJECT}/${RUNAI_JOB_NAME} --NotebookApp.token=''",
"image": "gcr.io/run-ai-lab/pytorch-example-jupyter",
"exposedUrls": [
{
"container": 8888,
"toolType": "jupyter-notebook",
"toolName": "Jupyter"
}
],
"compute": {
"gpuDevicesRequest": 1,
"gpuMemoryRequest": "4G",
"gpuMemoryLimit": "12G",
"largeShmRequest": true
}
"gpuDevicesRequest": 1,
"gpuMemoryRequest": "4G",
"gpuMemoryLimit": "12G",
"largeShmRequest": true

},
"exposedUrls" : [
{
"container" : 8888,
"toolType": "jupyter-notebook",
"toolName": "Jupyter"
}
]
}
}'
}
```

1. `<COMPANY-URL>` is the link to the Run:ai user interface.
2. `<TOKEN>` is the API access token obtained in Step 1.
3. `<PROJECT-ID>` is #The ID of the Project the workspace is running on. You can get the Project ID via the Get Projects API [Get Projects API](https://app.run.ai/api/docs#tag/Projects/operation/get_projects){target=_blank}.
4. `<CLUSTER-UUID>` is the unique identifier of the Cluster. You can get the Cluster UUID by adding the "Cluster ID" column to the Clusters view.
5. `toolType` will show the Jupyter icon when connecting to the Jupyter tool via the user interface.
6. `toolName` text will show when connecting to the Jupyter tool via the user interface.

!!! Note
The above API snippet runs with Run:ai clusters of 2.18 and above only.

Expand All @@ -157,49 +165,57 @@ Before you start, make sure:
After the workspace is created, it is added to the [workloads](../../platform-admin/workloads/overviews/managing-workloads.md) table

=== "CLI v2"
Copy the following command to your terminal. Make sure to update the below with the name of your project and workload:
Copy the following command to your terminal. Make sure to update the below with the name of the project and workload. Use the **project** where the previous workspace was created:

```sh
runai project set "project-name"
runai workspace submit "workload-name" --image gcr.io/run-ai-lab/pytorch-example-jupyter --gpu-memory-request 4G
--gpu-memory-limit 12G --large-shm --external-url container=8888
--name-prefix jupyter --command -- start-notebook.sh
--NotebookApp.base_url=/${RUNAI_PROJECT}/${RUNAI_JOB_NAME}
--NotebookApp.token=
runai workspace submit "workload-name" --image gcr.io/run-ai-lab/pytorch-example-jupyter --gpu-memory-request 4G \
--gpu-memory-limit 12G --large-shm --external-url container=8888 \
--name-prefix jupyter --command -- start-notebook.sh \
--NotebookApp.base_url=/${RUNAI_PROJECT}/${RUNAI_JOB_NAME} --NotebookApp.token=
```

=== "API"
Copy the following command to your terminal. Make sure to update the below parameters according to the comments. For more details, see [Workspaces API](https://api-docs.run.ai/latest/tag/Workspaces):
Copy the following command to your terminal. Make sure to update the below parameters according to the comments. Use the **project** and **cluster** where the previous workspace was created. For more details, see [Workspaces API](https://api-docs.run.ai/latest/tag/Workspaces):

```bash
curl -L 'https://<COMPANY-URL>/api/v1/workloads/workspaces' \ #<COMPANY-URL> is the link to the Run:ai user interface.
curl -L 'https://<COMPANY-URL>/api/v1/workloads/workspaces' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer <TOKEN>' \ #<TOKEN> is the API access token obtained in Step 1.
-H 'Authorization: Bearer <TOKEN>' \
-d '{
"name": "workload-name",
"projectId": "<PROJECT-ID>", '\ #The ID of the Project the workspace is running on. You can get the Project ID via the Get Projects API.
"clusterId": "<CLUSTER-UUID>", \ #<CLUSTER-UUID> is the unique identifier of the Cluster. You can get the Cluster UUID by adding the "Cluster ID" column to the Clusters view.
"projectId": "<PROJECT-ID>",
"clusterId": "<CLUSTER-UUID>",
"spec": {
"command" : "start-notebook.sh",
"args" : "--NotebookApp.base_url=/${RUNAI_PROJECT}/${RUNAI_JOB_NAME} --NotebookApp.token=''",
"image": "gcr.io/run-ai-lab/pytorch-example-jupyter",
"exposedUrls": [
{
"container": 8888,
"toolType": "jupyter-notebook",
"toolName": "Jupyter"
}
],
"compute": {
"gpuDevicesRequest": 1,
"gpuMemoryRequest": "4G",
"gpuMemoryLimit": "12G",
"largeShmRequest": true
}
"gpuDevicesRequest": 1,
"gpuMemoryRequest": "4G",
"gpuMemoryLimit": "12G",
"largeShmRequest": true

},
"exposedUrls" : [
{
"container" : 8888,
"toolType": "jupyter-notebook",
"toolName": "Jupyter"
}
]
}
}'
}
```

1. `<COMPANY-URL>` is the link to the Run:ai user interface.
2. `<TOKEN>` is the API access token obtained in Step 1.
3. `<PROJECT-ID>` is #The ID of the Project the workspace is running on. You can get the Project ID via the Get Projects API [Get Projects API](https://app.run.ai/api/docs#tag/Projects/operation/get_projects){target=_blank}.
4. `<CLUSTER-UUID>` is the unique identifier of the Cluster. You can get the Cluster UUID by adding the "Cluster ID" column to the Clusters view.
5. `toolType` will show the Jupyter icon when connecting to the Jupyter tool via the user interface.
6. `toolName` text will show when connecting to the Jupyter tool via the user interface.


!!! Note
The above API snippet runs with Run:ai clusters of 2.18 and above only.

Expand Down
60 changes: 34 additions & 26 deletions docs/Researcher/scheduling/launching-workloads-with-gpu-fractions.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -35,6 +35,7 @@ Before you start, make sure:
=== "API"
To use the API, you will need to obtain a token. Please follow the [API authentication](../../developer/rest-auth.md) article.


## Step 2: Submitting a workspace

=== "UI"
Expand Down Expand Up @@ -104,11 +105,10 @@ Before you start, make sure:

```sh
runai project set "project-name"
runai workspace submit "workload-name" --image jupyter/scipy-notebook
--gpu-devices-request 0.1 --command --external-url container=8888
--name-prefix jupyter --command -- start-notebook.sh
--NotebookApp.base_url=/${RUNAI_PROJECT}/${RUNAI_JOB_NAME}
--NotebookApp.token=
runai workspace submit "workload-name" --image jupyter/scipy-notebook \
--gpu-devices-request 0.1 --command --external-url container=8888 \
--name-prefix jupyter --command -- start-notebook.sh \
--NotebookApp.base_url=/${RUNAI_PROJECT}/${RUNAI_JOB_NAME} --NotebookApp.token=
```

This would start a workspace with a pre-configured Jupyter image with 10% of the GPU memory allocated.
Expand All @@ -125,37 +125,44 @@ Before you start, make sure:
This would start a workspace with a pre-configured Jupyter image with 10% of the GPU memory allocated.

=== "API"

Copy the following command to your terminal. Make sure to update the below parameters according to the comments. For more details, see [Workspaces API](https://api-docs.run.ai/latest/tag/Workspaces):

```sh
curl -L 'https://<COMPANY-URL>/api/v1/workloads/workspaces' \ #<COMPANY-URL> is the link to the Run:ai user interface.
```bash
curl -L 'https://<COMPANY-URL>/api/v1/workloads/workspaces' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer <TOKEN>' \ #<TOKEN> is the API access token obtained in Step 1.
-H 'Authorization: Bearer <TOKEN>' \
-d '{
"name": "workload-name",
"projectId": "<PROJECT-ID>", '\ #The ID of the Project the workspace is running on. You can get the Project ID via the Get Projects API.
"clusterId": "<CLUSTER-UUID>", \ #<CLUSTER-UUID> is the unique identifier of the Cluster. You can get the Cluster UUID by adding the "Cluster ID" column to the Clusters view.
"projectId": "<PROJECT-ID>",
"clusterId": "<CLUSTER-UUID>",
"spec": {
"command" : "start-notebook.sh",
"args" : "--NotebookApp.base_url=/${RUNAI_PROJECT}/${RUNAI_JOB_NAME} --NotebookApp.token=''",
"image": "jupyter/base-notebook",
"exposedUrls": [
{
"container": 8888,
"toolType": "jupyter-notebook",
"toolName": "Jupyter"
}
],
"image": "jupyter/scipy-notebook",
"compute": {
"gpuDevicesRequest": 1,
"gpuRequestType": "portion",
"gpuPortionRequest": 0.1

}
"gpuDevicesRequest": 1,
"gpuRequestType": "portion",
"gpuPortionRequest": 0.1

},
"exposedUrls" : [
{
"container" : 8888,
"toolType": "jupyter-notebook",
"toolName": "Jupyter"
}
]
}
}'
```
}
```

1. `<COMPANY-URL>` is the link to the Run:ai user interface.
2. `<TOKEN>` is the API access token obtained in Step 1.
3. `<PROJECT-ID>` is #The ID of the Project the workload is running on. You can get the Project ID via the Get Projects API [Get Projects API](https://app.run.ai/api/docs#tag/Projects/operation/get_projects){target=_blank}.
4. `<CLUSTER-UUID>` is the unique identifier of the Cluster. You can get the Cluster UUID by adding the "Cluster ID" column to the Clusters view.
5. `toolType` will show the Jupyter icon when connecting to the Jupyter tool via the user interface.
6. `toolName` text will show when connecting to the Jupyter tool via the user interface.


This would start a workspace with a pre-configured Jupyter image with 10% of the GPU memory allocated.

Expand All @@ -182,6 +189,7 @@ Before you start, make sure:

=== "API"
To connect to the Jupyter Notebook, browse directly to `https://<COMPANY-URL>/<PROJECT-NAME>/<WORKLOAD_NAME>`


## Next Steps

Expand Down
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